Who should use the Model Quantization workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for model quantization with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
Quantized model live in production with monitoring and rollback plan
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Quantized model live in production with monitoring and rollback plan
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use ONNX Runtime to clear model and quantization strategy defined with calibration data ready. Then, you pass the output to Captum to sensitivity map created; quantization plan adjusted for accuracy-critical layers. Then, you pass the output to ONNX Runtime to quantized model produced with reduced memory footprint and faster inference. Then, you pass the output to Deepchecks to accuracy impact quantified; decision made to accept, retune, or switch to qat. Then, you pass the output to ONNX Runtime to performance gains quantified: latency reduction, throughput increase, memory savings. Finally, MLRun is used to quantized model live in production with monitoring and rollback plan.
Select Target Model and Quantization Type
Clear model and quantization strategy defined with calibration data ready
Profile Model for Sensitivity Analysis
Sensitivity map created; quantization plan adjusted for accuracy-critical layers
Apply Post-Training Quantization (PTQ)
Quantized model produced with reduced memory footprint and faster inference
Validate Quantized Model Accuracy
Accuracy impact quantified; decision made to accept, retune, or switch to QAT
Benchmark Inference Performance
Performance gains quantified: latency reduction, throughput increase, memory savings
Deploy Quantized Model to Production
Quantized model live in production with monitoring and rollback plan
Identify the pre-trained model to quantize (e.g., a PyTorch or TensorFlow model) and choose the quantization approach: post-training quantization (PTQ) or quantization-aware training (QAT). For PTQ, decide between weight-only, dynamic, or integer-only quantization based on hardware constraints (CPU, GPU, edge device).
Why ONNX Runtime: ONNX Runtime provides model quantization capabilities and supports ONNX models, which can be converted from PyTorch/TensorFlow, and it includes calibration dataset loading utilities for quantization.
Run inference on a small batch to measure layer-wise weight and activation ranges. Use tools like torch.profiler or TensorFlow Model Analysis to identify outlier layers that may degrade accuracy after quantization. This step informs whether to skip quantization on certain layers or use mixed-precision.
Why Captum: Captum provides feature importance attribution and model debugging tools for PyTorch models, which can be used for sensitivity analysis to understand which layers/parameters are most sensitive to quantization.
Use framework-native quantization APIs (e.g., torch.quantization.quantize_dynamic, TensorFlow Lite Converter) to convert model weights and activations to lower precision. For static quantization, calibrate scale/zero-point using the calibration dataset. Export the quantized model to an optimized format (e.g., ONNX, TFLite, Core ML).
Why ONNX Runtime: ONNX Runtime has built-in model quantization tools that support post-training quantization (PTQ) for ONNX models, including dynamic and static quantization.
Run the quantized model on a held-out validation set and compare metrics (e.g., accuracy, F1, perplexity) against the full-precision baseline. Use tools like torchmetrics or custom evaluation scripts. If accuracy drops beyond an acceptable threshold (e.g., >1%), consider switching to QAT or adjusting mixed-precision layers.
Why Deepchecks: Deepchecks offers model evaluation and comparison capabilities, allowing validation of quantized model accuracy against the original model using validation datasets.
Measure latency, throughput, and memory usage of the quantized model on target hardware (CPU, GPU, or edge device). Use benchmarking tools like ONNX Runtime perf, TensorFlow Lite benchmark, or custom timing loops. Compare against full-precision model to confirm speedup and memory reduction.
Why ONNX Runtime: ONNX Runtime includes benchmarking tools for measuring inference performance, latency, and throughput of quantized models.
Package the quantized model into a serving container or edge runtime (e.g., TensorFlow Serving, TorchServe, ONNX Runtime Server). Integrate with existing inference pipeline, ensuring input/output compatibility. Set up monitoring for inference latency and accuracy drift in production.
Why MLRun: MLRun provides real-time serverless model serving and automated experiment tracking, supporting deployment of quantized models with monitoring capabilities.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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